Since the earliest use of insulin for treatment of diabetes, efforts have been made to adjust the dosages of insulin based on clinical experience, and more particularly, measurements of the level of glucose. Initially glucose tests were done infrequently and in a standard clinical laboratory. With the advent of intermittent self-monitored glucose testing (i.e., self-monitoring blood glucose (SMBG)), such testing could be done by the patient and with a greater frequency at low cost. The application of information derived from more frequent glucose testing has allowed significantly better glucose control, and has lowered the occurrence of complications due to poor glycemic control. About a decade ago, the art incorporated continuous glucose monitors (i.e., continuous glucose monitoring (CGM)) that deliver glucose readings every few minutes. The results were displayed to the patient, and variously provided indications of the trend of the glucose as well as high-glucose and low-glucose alarms. Technological advances have been made also in the development of insulin pumps, which can replace multiple daily self-injections of insulin. These currently available devices can deliver precise insulin dosages, typically on a programmable schedule which may be adjustable on the basis of input from the user or healthcare professional, or on the basis of data from a continuous glucose monitor.
Basic algorithms have been developed that estimate an appropriate insulin dosing schedule based, for example, on patient weight, and these algorithms provide a reasonable first approximation of a clinically appropriate insulin-dosing schedule. There is, however, considerable variation among patients with regard to their metabolism and responsiveness to insulin.
Various approaches have been applied to making calculations that use continuous glucose monitor (CGM) data to improve or adjust insulin dosing. Artificial pancreas algorithms attempt to regulate blood glucose concentration in the face of meal disturbances and physical activity.
Other approaches, for example, provide for setting a basal insulin dose based on consideration of a patient's history, particularly glucose excursion data over a period of time.
Nevertheless, in spite of current aspects of diabetes care management, tight glycemic control has yet to be achieved. Insulin pump shut-off algorithms, as have been described in the prior art, use CGM data to inform the decision to completely stop the flow of insulin based on a prediction of hypoglycaemia. This approach has been shown to reduce the risk of nocturnal hypoglycaemia. A possible drawback is that the use of an on-off control law for basal insulin, similar to bang-bang or relay control, may induce undesired oscillations of plasma glucose. In fact, if the basal insulin is higher than that needed to keep the glycemic target, the recovery from hypoglycemia would be followed by application of the basal that will cause a new shut-off occurrence. The cycle of shut-off interventions yields an insulin square wave that induces periodic oscillation of plasma glucose.